{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "MDyR63OTNUJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3e0f9ff-370f-46a3-8496-937e6abdae76"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m908.2/908.2 MB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 MB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m27.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m336.6/336.6 kB\u001b[0m \u001b[31m33.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m38.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m374.8/374.8 kB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m313.9/313.9 kB\u001b[0m \u001b[31m33.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.7/8.7 MB\u001b[0m \u001b[31m109.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m127.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests==2.32.4, but you have requests 2.32.3 which is incompatible.\n",
            "gcsfs 2025.3.0 requires fsspec==2025.3.0, but you have fsspec 2024.9.0 which is incompatible.\n",
            "google-adk 1.16.0 requires requests<3.0.0,>=2.32.4, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "# pip installs\n",
        "\n",
        "!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n",
        "!pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U bitsandbytes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAe3PU4Hhcy8",
        "outputId": "7a1382b1-253c-4ef3-8f11-f553adf9d35b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.12/dist-packages (0.46.0)\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.48.1-py3-none-manylinux_2_24_x86_64.whl.metadata (10 kB)\n",
            "Requirement already satisfied: torch<3,>=2.3 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (2.5.1+cu124)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (2.0.2)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from bitsandbytes) (25.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.20.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (4.15.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.5)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.1.6)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (2024.9.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (9.1.0.70)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.5.8)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (11.2.1.3)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (10.3.5.147)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (11.6.1.9)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.3.1.170)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (2.21.5)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (12.4.127)\n",
            "Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (3.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (75.2.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.12/dist-packages (from torch<3,>=2.3->bitsandbytes) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy==1.13.1->torch<3,>=2.3->bitsandbytes) (1.3.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch<3,>=2.3->bitsandbytes) (3.0.3)\n",
            "Downloading bitsandbytes-0.48.1-py3-none-manylinux_2_24_x86_64.whl (60.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.1/60.1 MB\u001b[0m \u001b[31m40.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "  Attempting uninstall: bitsandbytes\n",
            "    Found existing installation: bitsandbytes 0.46.0\n",
            "    Uninstalling bitsandbytes-0.46.0:\n",
            "      Successfully uninstalled bitsandbytes-0.46.0\n",
            "Successfully installed bitsandbytes-0.48.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "-yikV8pRBer9"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import re\n",
        "import math\n",
        "from tqdm import tqdm\n",
        "from google.colab import userdata\n",
        "from huggingface_hub import login\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import transformers\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, set_seed, BitsAndBytesConfig\n",
        "from datasets import load_dataset, Dataset, DatasetDict\n",
        "from datetime import datetime\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer, SFTConfig, DataCollatorForCompletionOnlyLM\n",
        "import matplotlib.pyplot as plt\n",
        "import wandb\n",
        "from peft import LoraConfig\n",
        "from trl import SFTTrainer, SFTConfig\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "uuTX-xonNeOK"
      },
      "outputs": [],
      "source": [
        "# Constants\n",
        "\n",
        "BASE_MODEL = \"meta-llama/Llama-3.2-1B\"\n",
        "PROJECT_NAME = \"pricer-2025\"\n",
        "HF_USER = \"javedumar507\" # your HF name here!\n",
        "\n",
        "# Data\n",
        "\n",
        "# DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
        "# Or just use the one I've uploaded\n",
        "DATASET_NAME = \"ed-donner/pricer-data\"\n",
        "MAX_SEQUENCE_LENGTH = 182\n",
        "\n",
        "# Run name for saving the model in the hub\n",
        "\n",
        "RUN_NAME =  \"pricer-2025\"\n",
        "PROJECT_RUN_NAME = \"pricer-2025\"\n",
        "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
        "\n",
        "# Hyperparameters for QLoRA\n",
        "\n",
        "LORA_R = 16\n",
        "LORA_ALPHA = 32\n",
        "TARGET_MODULES = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\", \"up_proj\", \"down_proj\"]\n",
        "LORA_DROPOUT = 0.1\n",
        "QUANT_4_BIT = True\n",
        "\n",
        "# Hyperparameters for Training\n",
        "\n",
        "EPOCHS = 1 # you can do more epochs if you wish, but only 1 is needed - more is probably overkill\n",
        "BATCH_SIZE = 4 # on an A100 box this can go up to 16\n",
        "GRADIENT_ACCUMULATION_STEPS = 1\n",
        "LEARNING_RATE = 5e-5\n",
        "LR_SCHEDULER_TYPE = 'cosine'\n",
        "WARMUP_RATIO = 0.01\n",
        "OPTIMIZER = \"paged_adamw_32bit\"\n",
        "\n",
        "# Admin config - note that SAVE_STEPS is how often it will upload to the hub\n",
        "# I've changed this from 5000 to 2000 so that you get more frequent saves\n",
        "\n",
        "STEPS = 50\n",
        "SAVE_STEPS = 2000\n",
        "LOG_TO_WANDB = True\n",
        "\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "QyHOj-c4FmkM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "518f5fc9-74cb-495e-d048-82d469bca9bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'javedumar507/pricer-2025'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "HUB_MODEL_NAME"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8JArT3QAQAjx"
      },
      "source": [
        "### Log in to HuggingFace and Weights & Biases\n",
        "\n",
        "If you don't already have a HuggingFace account, visit https://huggingface.co to sign up and create a token.\n",
        "\n",
        "Then select the Secrets for this Notebook by clicking on the key icon in the left, and add a new secret called `HF_TOKEN` with the value as your token.\n",
        "\n",
        "Repeat this for weightsandbiases at https://wandb.ai and add a secret called `WANDB_API_KEY`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "WyFPZeMcM88v"
      },
      "outputs": [],
      "source": [
        "# Log in to HuggingFace\n",
        "\n",
        "hf_token = userdata.get('HF_TOKEN')\n",
        "login(hf_token, add_to_git_credential=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "yJNOv3cVvJ68",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59294f1e-d68b-4a89-e2d6-046d18d31585"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Calling wandb.login() after wandb.init() has no effect.\n"
          ]
        }
      ],
      "source": [
        "# Log in to Weights & Biases\n",
        "wandb_api_key = userdata.get('WANDB_API_KEY')\n",
        "os.environ[\"WANDB_API_KEY\"] = wandb_api_key\n",
        "wandb.login()\n",
        "\n",
        "# Configure Weights & Biases to record against our project\n",
        "os.environ[\"WANDB_PROJECT\"] = PROJECT_NAME\n",
        "os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\" if LOG_TO_WANDB else \"end\"\n",
        "os.environ[\"WANDB_WATCH\"] = \"gradients\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "cvXVoJH8LS6u",
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "18a123e4bc044ec6b146dce93e1a642c",
            "c6d199e9c5b84e459f6eb93da4866b0c"
          ]
        },
        "outputId": "525a5f0d-987b-41ca-a2e6-155008691bf2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/416 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f69f38011bd4908b3b506de768a213c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/train-00000-of-00001.parquet:   0%|          | 0.00/185M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e919597f24a4f1cabed2176944a5192"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/test-00000-of-00001.parquet:   0%|          | 0.00/914k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "649570caae194bcf857748556eee2e11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/400000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24e931de04e543dba829d1fa5c5933e9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a28832ba2caf4fc2932cb537badd451e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = load_dataset(DATASET_NAME)\n",
        "train = dataset['train']\n",
        "test = dataset['test'].select(range(1000)) if dataset['test'] > 1000 else dataset['test']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "rJb9IDVjOAn9"
      },
      "outputs": [],
      "source": [
        "# if you wish to reduce the training dataset to 20,000 points instead, then uncomment this line:\n",
        "train = train.select(range(20000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "8_SUsKqA23Gc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "059298ea-557e-4ed2-fab1-91be03cb1813"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Finishing previous runs because reinit is set to 'default'."
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">2025-10-28_06.58.10</strong> at: <a href='https://wandb.ai/javedumar507-research/gpt-pricer/runs/s5vajgd4' target=\"_blank\">https://wandb.ai/javedumar507-research/gpt-pricer/runs/s5vajgd4</a><br> View project at: <a href='https://wandb.ai/javedumar507-research/gpt-pricer' target=\"_blank\">https://wandb.ai/javedumar507-research/gpt-pricer</a><br>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20251028_065834-s5vajgd4/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.22.2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20251028_071438-tsx3d094</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/javedumar507-research/pricer-2025/runs/tsx3d094' target=\"_blank\">pricer-2025</a></strong> to <a href='https://wandb.ai/javedumar507-research/pricer-2025' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/javedumar507-research/pricer-2025' target=\"_blank\">https://wandb.ai/javedumar507-research/pricer-2025</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/javedumar507-research/pricer-2025/runs/tsx3d094' target=\"_blank\">https://wandb.ai/javedumar507-research/pricer-2025/runs/tsx3d094</a>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "if LOG_TO_WANDB:\n",
        "  wandb.init(project=PROJECT_NAME, name=RUN_NAME)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJWQ0a3wZ0Bw"
      },
      "source": [
        "## Now load the Tokenizer and Model\n",
        "\n",
        "The model is \"quantized\" - we are reducing the precision to 4 bits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "9lb7M9xn46wx"
      },
      "outputs": [],
      "source": [
        "# pick the right quantization\n",
        "\n",
        "if QUANT_4_BIT:\n",
        "  quant_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "    bnb_4bit_quant_type=\"nf4\"\n",
        "  )\n",
        "else:\n",
        "  quant_config = BitsAndBytesConfig(\n",
        "    load_in_8bit=True,\n",
        "    bnb_8bit_compute_dtype=torch.bfloat16\n",
        "  )"
      ]
    },
    {
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        "outputId": "105d9e8c-b715-4e26-8adf-e21e3545aada"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/50.5k [00:00<?, ?B/s]"
            ],
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
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              "special_tokens_map.json:   0%|          | 0.00/301 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6107381bb0a6448895f97ab7fcd05992"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/843 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d95f0e7af6ad4dceae9c73f2d192f62b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.47G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "035bc548bc57410aa9d5d8ac382f9236"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/185 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffbe933483504cd48586fd97801f211a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Memory footprint: 1012.0 MB\n"
          ]
        }
      ],
      "source": [
        "# Load the Tokenizer and the Model\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    BASE_MODEL,\n",
        "    quantization_config=quant_config,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n",
        "\n",
        "print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e6:.1f} MB\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "2omVEaPIVJZa"
      },
      "outputs": [],
      "source": [
        "from trl import DataCollatorForCompletionOnlyLM\n",
        "response_template = \"Price is $\"\n",
        "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DaOeBhyy9eS"
      },
      "source": [
        "# AND NOW\n",
        "\n",
        "## We set up the configuration for Training\n",
        "\n",
        "We need to create 2 objects:\n",
        "\n",
        "A LoraConfig object with our hyperparameters for LoRA\n",
        "\n",
        "An SFTConfig with our overall Training parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "fCwmDmkSATvj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "db2eedf5b79d425a951822e487eb79e9",
            "844a4cfe086e41e68a987cc945acab1a",
            "a5898a0f58f742718d05a4c281f4935b",
            "1f4cd3e4aa304ada86278b2f70563222",
            "37d5b410da484aec861ce1ab33665690",
            "a719729f61d4450093c47c66300c47ea",
            "26246e48d2f3414aa60f04c0426b228b",
            "e49aa15d9c704a56b23a0b5b70e54bcb",
            "218392fbab1941868d7e914ff6b116a5",
            "588c30fe11244ea2818853c80f32f6b6",
            "f96b27a37ae9486ea262d4e80cc0479a"
          ]
        },
        "outputId": "3fda99e6-17f3-4a7c-9745-fca8e2e11524"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/20000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "db2eedf5b79d425a951822e487eb79e9"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# First, specify the configuration parameters for LoRA\n",
        "\n",
        "lora_parameters = LoraConfig(\n",
        "    lora_alpha=LORA_ALPHA,\n",
        "    lora_dropout=LORA_DROPOUT,\n",
        "    r=LORA_R,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    target_modules=TARGET_MODULES,\n",
        ")\n",
        "\n",
        "# Next, specify the general configuration parameters for training\n",
        "\n",
        "train_parameters = SFTConfig(\n",
        "    output_dir=PROJECT_RUN_NAME,\n",
        "    num_train_epochs=EPOCHS,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=1,\n",
        "    eval_strategy=\"no\",\n",
        "    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
        "    optim=OPTIMIZER,\n",
        "    save_steps=SAVE_STEPS,\n",
        "    save_total_limit=10,\n",
        "    logging_steps=STEPS,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=True,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=WARMUP_RATIO,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
        "    report_to=\"wandb\" if LOG_TO_WANDB else None,\n",
        "    run_name=RUN_NAME,\n",
        "    max_seq_length=MAX_SEQUENCE_LENGTH,\n",
        "    dataset_text_field=\"text\",\n",
        "    save_strategy=\"steps\",\n",
        "    hub_model_id=HUB_MODEL_NAME,\n",
        "    hub_private_repo=False\n",
        ")\n",
        "\n",
        "# And now, the Supervised Fine Tuning Trainer will carry out the fine-tuning\n",
        "# Given these 2 sets of configuration parameters\n",
        "# The latest version of trl is showing a warning about labels - please ignore this warning\n",
        "# But let me know if you don't see good training results (loss coming down).\n",
        "\n",
        "fine_tuning = SFTTrainer(\n",
        "    model=base_model,\n",
        "    train_dataset=train,\n",
        "    peft_config=lora_parameters,\n",
        "    args=train_parameters,\n",
        "    data_collator=collator\n",
        "  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "GfvAxnXPvB7w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "658d975b-a1b3-4d0e-b91e-866e9dac2f3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5000' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5000/5000 24:50, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.178300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.968800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>2.047600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>2.016900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>2.057700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.997300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>2.010400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>2.005600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.994000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.961400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.963500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.956100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.957400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.971700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.946700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.937300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.977200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.003100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.996100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.988400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.958900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.903000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>1.992300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.930400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>1.977400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>2.016200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>1.915800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.963400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1450</td>\n",
              "      <td>1.957300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.958500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1550</td>\n",
              "      <td>1.899100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>1.963700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1650</td>\n",
              "      <td>1.871900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>1.958800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>1.948800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.923200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1850</td>\n",
              "      <td>1.881300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>1.956200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1950</td>\n",
              "      <td>1.959100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.923100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2050</td>\n",
              "      <td>1.939600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.904500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2150</td>\n",
              "      <td>1.897500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>1.917200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2250</td>\n",
              "      <td>1.956500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>1.959200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2350</td>\n",
              "      <td>1.896900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.893300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2450</td>\n",
              "      <td>1.871600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.913300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2550</td>\n",
              "      <td>1.907500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>1.921800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2650</td>\n",
              "      <td>1.930500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>1.932400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2750</td>\n",
              "      <td>1.906700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>1.921700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2850</td>\n",
              "      <td>1.887700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>1.919700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2950</td>\n",
              "      <td>1.921100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.893000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3050</td>\n",
              "      <td>1.947900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>1.851500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3150</td>\n",
              "      <td>1.937500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3200</td>\n",
              "      <td>1.868100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3250</td>\n",
              "      <td>1.885000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>1.879500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3350</td>\n",
              "      <td>1.871000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3400</td>\n",
              "      <td>1.868900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3450</td>\n",
              "      <td>1.876600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.881100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3550</td>\n",
              "      <td>1.830400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>1.860900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3650</td>\n",
              "      <td>1.865200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3700</td>\n",
              "      <td>1.899300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3750</td>\n",
              "      <td>1.889100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3800</td>\n",
              "      <td>1.907400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3850</td>\n",
              "      <td>1.875000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>1.932700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3950</td>\n",
              "      <td>1.927300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>1.871700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4050</td>\n",
              "      <td>1.866000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4100</td>\n",
              "      <td>1.904300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4150</td>\n",
              "      <td>1.861900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>1.854600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4250</td>\n",
              "      <td>1.913600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4300</td>\n",
              "      <td>1.843600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4350</td>\n",
              "      <td>1.882200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4400</td>\n",
              "      <td>1.867300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4450</td>\n",
              "      <td>1.852700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.902500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4550</td>\n",
              "      <td>1.920700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4600</td>\n",
              "      <td>1.860300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4650</td>\n",
              "      <td>1.842900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4700</td>\n",
              "      <td>1.859000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4750</td>\n",
              "      <td>1.860000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>1.841400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4850</td>\n",
              "      <td>1.900200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4900</td>\n",
              "      <td>1.862700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4950</td>\n",
              "      <td>1.883300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>1.853800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (pricer-2025/checkpoint-2000)... Done. 0.7s\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (pricer-2025/checkpoint-4000)... Done. 0.8s\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (pricer-2025/checkpoint-5000)... Done. 0.8s\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "HfHubHTTPError",
          "evalue": "(Request ID: Root=1-69007ed5-0756ad9c3deaf03e6d554d62;4d057838-b46f-4dde-8057-1fa4277d44ab)\n\n403 Forbidden: You don't have the rights to create a model under the namespace \"javedumar507\".\nCannot access content at: https://huggingface.co/api/repos/create.\nMake sure your token has the correct permissions.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_http.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    406\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    408\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-1834539659.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfine_tuning\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfine_tuning\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHUB_MODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhf_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHUB_MODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhf_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Saved to the hub: {HUB_MODEL_NAME}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, repo_id, use_temp_dir, commit_message, private, token, max_shard_size, create_pr, safe_serialization, revision, commit_description, tags, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m    931\u001b[0m         \u001b[0morganization\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeprecated_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"organization\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m         repo_id = self._create_repo(\n\u001b[0m\u001b[1;32m    934\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepo_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morganization\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morganization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/utils/hub.py\u001b[0m in \u001b[0;36m_create_repo\u001b[0;34m(self, repo_id, private, token, repo_url, organization)\u001b[0m\n\u001b[1;32m    738\u001b[0m                 \u001b[0mrepo_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{organization}/{repo_id}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 740\u001b[0;31m         \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprivate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprivate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    741\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, resource_group_id, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3777\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mRepoUrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.endpoint}/{repo_type}/{repo_id}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3778\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3779\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3780\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3781\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, resource_group_id, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   3764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3765\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3766\u001b[0;31m             \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3767\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3768\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mexist_ok\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m409\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_http.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    469\u001b[0m                 \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\nMake sure your token has the correct permissions.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m             )\n\u001b[0;32m--> 471\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0m_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m416\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m: (Request ID: Root=1-69007ed5-0756ad9c3deaf03e6d554d62;4d057838-b46f-4dde-8057-1fa4277d44ab)\n\n403 Forbidden: You don't have the rights to create a model under the namespace \"javedumar507\".\nCannot access content at: https://huggingface.co/api/repos/create.\nMake sure your token has the correct permissions."
          ]
        }
      ],
      "source": [
        "fine_tuning.train()\n",
        "\n",
        "fine_tuning.model.push_to_hub(HUB_MODEL_NAME, token=hf_token)\n",
        "tokenizer.push_to_hub(HUB_MODEL_NAME, token=hf_token)\n",
        "print(f\"Saved to the hub: {HUB_MODEL_NAME}\")\n",
        "# uploading to Hugging face have some issues will resolve later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "32vvrYRVAUNg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "outputId": "e569d319-5644-441b-b4c2-7b0beffce219"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<br>    <style><br>        .wandb-row {<br>            display: flex;<br>            flex-direction: row;<br>            flex-wrap: wrap;<br>            justify-content: flex-start;<br>            width: 100%;<br>        }<br>        .wandb-col {<br>            display: flex;<br>            flex-direction: column;<br>            flex-basis: 100%;<br>            flex: 1;<br>            padding: 10px;<br>        }<br>    </style><br><div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂▂▃▃▃▃▄▄▅▅▅▅▅▆▆▆▆▆▇▇▇▇█</td></tr><tr><td>train/global_step</td><td>▁▁▁▂▃▃▄▄▄▄▅▅▅▅▁▁▁▂▂▂▃▃▃▃▃▄▄▄▅▅▅▅▆▆▆▆▇▇▇█</td></tr><tr><td>train/grad_norm</td><td>▄▄▃▂▅▂▂▂▁▂▂▂▂▁▂█▂▂▃▂▂▁▂▁▂▁▂▂▂▁▂▁▁▃▁▂▁▄▂▂</td></tr><tr><td>train/learning_rate</td><td>▁▃▃▅▆████████████████▇▇▇▇▆▆▆▆▅▃▂▂▂▂▁▁▁▁▁</td></tr><tr><td>train/loss</td><td>█▂▂▂▁▂▂▂▂▂▂▂▁▂▂▁▂▂▃▂▂▂▂▂▂▂▂▂▂▁▂▁▁▁▁▁▁▁▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>total_flos</td><td>2.149777988542464e+16</td></tr><tr><td>train/epoch</td><td>1</td></tr><tr><td>train/global_step</td><td>5000</td></tr><tr><td>train/grad_norm</td><td>4.05501</td></tr><tr><td>train/learning_rate</td><td>0</td></tr><tr><td>train/loss</td><td>1.8538</td></tr><tr><td>train_loss</td><td>1.92245</td></tr><tr><td>train_runtime</td><td>1415.4656</td></tr><tr><td>train_samples_per_second</td><td>14.13</td></tr><tr><td>train_steps_per_second</td><td>3.532</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">pricer-2025</strong> at: <a href='https://wandb.ai/javedumar507-research/pricer-2025/runs/tsx3d094' target=\"_blank\">https://wandb.ai/javedumar507-research/pricer-2025/runs/tsx3d094</a><br> View project at: <a href='https://wandb.ai/javedumar507-research/pricer-2025' target=\"_blank\">https://wandb.ai/javedumar507-research/pricer-2025</a><br>Synced 5 W&B file(s), 0 media file(s), 42 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20251028_071438-tsx3d094/logs</code>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "if LOG_TO_WANDB:\n",
        "  wandb.finish()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pXOyEyxhJTD",
        "outputId": "40453a9e-0aa4-4700-dc39-4807da29c1f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuned model loaded from local checkpoint. Memory: 1150.6 MB\n"
          ]
        }
      ],
      "source": [
        "# Load from LOCAL checkpoint\n",
        "LOCAL_CHECKPOINT = \"pricer-2025/checkpoint-5000\"\n",
        "\n",
        "fine_tuned_model = PeftModel.from_pretrained(base_model, LOCAL_CHECKPOINT)\n",
        "print(f\"Fine-tuned model loaded from local checkpoint. Memory: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "kErxMlIvhJTD"
      },
      "outputs": [],
      "source": [
        "def extract_price(s):\n",
        "    if \"Price is $\" in s:\n",
        "        contents = s.split(\"Price is $\")[1]\n",
        "        contents = contents.replace(',','')\n",
        "        match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", contents)\n",
        "        return float(match.group()) if match else 0\n",
        "    return 0\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "4IsBjC78hJTE"
      },
      "outputs": [],
      "source": [
        "def model_predict(prompt):\n",
        "    set_seed(42)\n",
        "    inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    attention_mask = torch.ones(inputs.shape, device=\"cuda\")\n",
        "    outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=3, num_return_sequences=1)\n",
        "    response = tokenizer.decode(outputs[0])\n",
        "    return extract_price(response)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "Agcn2am7hJTE"
      },
      "outputs": [],
      "source": [
        "top_K = 3\n",
        "\n",
        "def improved_model_predict(prompt, device=\"cuda\"):\n",
        "    set_seed(42)\n",
        "    inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n",
        "    attention_mask = torch.ones(inputs.shape, device=device)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n",
        "        next_token_logits = outputs.logits[:, -1, :].to('cpu')\n",
        "\n",
        "    next_token_probs = F.softmax(next_token_logits, dim=-1)\n",
        "    top_prob, top_token_id = next_token_probs.topk(top_K)\n",
        "    prices, weights = [], []\n",
        "    for i in range(top_K):\n",
        "        predicted_token = tokenizer.decode(top_token_id[0][i])\n",
        "        probability = top_prob[0][i]\n",
        "        try:\n",
        "            result = float(predicted_token)\n",
        "        except ValueError as e:\n",
        "            result = 0.0\n",
        "        if result > 0:\n",
        "            prices.append(result)\n",
        "            weights.append(probability)\n",
        "    if not prices:\n",
        "        return 0.0\n",
        "    total = sum(weights)\n",
        "    weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n",
        "    return sum(weighted_prices).item()\n",
        "\n",
        "fine_tuned_model = fine_tuned_model.float()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "jo1ohRk-hJTE"
      },
      "outputs": [],
      "source": [
        "GREEN = \"\\033[92m\"\n",
        "YELLOW = \"\\033[93m\"\n",
        "RED = \"\\033[91m\"\n",
        "RESET = \"\\033[0m\"\n",
        "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}\n",
        "\n",
        "class Tester:\n",
        "\n",
        "    def __init__(self, predictor, data, title=None, size=250):\n",
        "        self.predictor = predictor\n",
        "        self.data = data\n",
        "        self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
        "        self.size = size\n",
        "        self.guesses = []\n",
        "        self.truths = []\n",
        "        self.errors = []\n",
        "        self.sles = []\n",
        "        self.colors = []\n",
        "\n",
        "    def color_for(self, error, truth):\n",
        "        if error<40 or error/truth < 0.2:\n",
        "            return \"green\"\n",
        "        elif error<80 or error/truth < 0.4:\n",
        "            return \"orange\"\n",
        "        else:\n",
        "            return \"red\"\n",
        "\n",
        "    def run_datapoint(self, i):\n",
        "        datapoint = self.data[i]\n",
        "        guess = self.predictor(datapoint[\"text\"])\n",
        "        truth = datapoint[\"price\"]\n",
        "        error = abs(guess - truth)\n",
        "        log_error = math.log(truth+1) - math.log(guess+1)\n",
        "        sle = log_error ** 2\n",
        "        color = self.color_for(error, truth)\n",
        "        title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n",
        "        self.guesses.append(guess)\n",
        "        self.truths.append(truth)\n",
        "        self.errors.append(error)\n",
        "        self.sles.append(sle)\n",
        "        self.colors.append(color)\n",
        "        print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
        "\n",
        "    def chart(self, title):\n",
        "        max_error = max(self.errors)\n",
        "        plt.figure(figsize=(12, 8))\n",
        "        max_val = max(max(self.truths), max(self.guesses))\n",
        "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
        "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
        "        plt.xlabel('Ground Truth')\n",
        "        plt.ylabel('Model Estimate')\n",
        "        plt.xlim(0, max_val)\n",
        "        plt.ylim(0, max_val)\n",
        "        plt.title(title)\n",
        "        plt.show()\n",
        "\n",
        "    def report(self):\n",
        "        average_error = sum(self.errors) / self.size\n",
        "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
        "        hits = sum(1 for color in self.colors if color==\"green\")\n",
        "        title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
        "        self.chart(title)\n",
        "\n",
        "    def run(self):\n",
        "        self.error = 0\n",
        "        for i in range(self.size):\n",
        "            self.run_datapoint(i)\n",
        "        self.report()\n",
        "\n",
        "    @classmethod\n",
        "    def test(cls, function, data):\n",
        "        cls(function, data).run()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TLvU-L7DhJTE",
        "outputId": "e5657a0c-81de-414f-ef6d-c52355973ebe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[93m1: Guess: $276.51 Truth: $374.41 Error: $97.90 SLE: 0.09 Item: OEM AC Compressor w/...\u001b[0m\n",
            "\u001b[93m2: Guess: $152.92 Truth: $225.11 Error: $72.19 SLE: 0.15 Item: Motorcraft YB3125 Fa...\u001b[0m\n",
            "\u001b[92m3: Guess: $53.93 Truth: $61.68 Error: $7.75 SLE: 0.02 Item: Dorman Front Washer ...\u001b[0m\n",
            "\u001b[91m4: Guess: $347.58 Truth: $599.99 Error: $252.41 SLE: 0.30 Item: HP Premium HD Plus T...\u001b[0m\n",
            "\u001b[92m5: Guess: $35.72 Truth: $16.99 Error: $18.73 SLE: 0.51 Item: Super Switch Pickup ...\u001b[0m\n",
            "\u001b[92m6: Guess: $8.02 Truth: $31.99 Error: $23.97 SLE: 1.68 Item: Horror Bookmarks, Re...\u001b[0m\n",
            "\u001b[92m7: Guess: $117.70 Truth: $101.79 Error: $15.91 SLE: 0.02 Item: SK6241 - Stinger 4 G...\u001b[0m\n",
            "\u001b[92m8: Guess: $311.53 Truth: $289.00 Error: $22.53 SLE: 0.01 Item: Godox ML60Bi LED Lig...\u001b[0m\n",
            "\u001b[93m9: Guess: $429.95 Truth: $635.86 Error: $205.91 SLE: 0.15 Item: Randall G3 Plus Comb...\u001b[0m\n",
            "\u001b[92m10: Guess: $70.19 Truth: $65.99 Error: $4.20 SLE: 0.00 Item: HOLDWILL 6 Pack LED ...\u001b[0m\n",
            "\u001b[93m11: Guess: $350.94 Truth: $254.21 Error: $96.73 SLE: 0.10 Item: Viking Horns 3 Gallo...\u001b[0m\n",
            "\u001b[91m12: Guess: $182.63 Truth: $412.99 Error: $230.36 SLE: 0.66 Item: CURT 70110 Custom To...\u001b[0m\n",
            "\u001b[91m13: Guess: $59.96 Truth: $205.50 Error: $145.54 SLE: 1.49 Item: Solar HAMMERED BRONZ...\u001b[0m\n",
            "\u001b[92m14: Guess: $232.35 Truth: $248.23 Error: $15.88 SLE: 0.00 Item: COSTWAY Electric Tum...\u001b[0m\n",
            "\u001b[91m15: Guess: $228.82 Truth: $399.00 Error: $170.18 SLE: 0.31 Item: FREE SIGNAL TV Trans...\u001b[0m\n",
            "\u001b[92m16: Guess: $343.89 Truth: $373.94 Error: $30.05 SLE: 0.01 Item: Bilstein 5100 Monotu...\u001b[0m\n",
            "\u001b[93m17: Guess: $159.43 Truth: $92.89 Error: $66.54 SLE: 0.29 Item: Sangean K-200 Multi-...\u001b[0m\n",
            "\u001b[92m18: Guess: $35.35 Truth: $51.99 Error: $16.64 SLE: 0.14 Item: Charles Leonard Magn...\u001b[0m\n",
            "\u001b[91m19: Guess: $283.11 Truth: $179.00 Error: $104.11 SLE: 0.21 Item: Gigabyte AMD Radeon ...\u001b[0m\n",
            "\u001b[92m20: Guess: $6.29 Truth: $19.42 Error: $13.13 SLE: 1.06 Item: 3dRose LLC 8 x 8 x 0...\u001b[0m\n",
            "\u001b[92m21: Guess: $462.90 Truth: $539.95 Error: $77.05 SLE: 0.02 Item: ROKINON 85mm F1.4 Au...\u001b[0m\n",
            "\u001b[92m22: Guess: $153.28 Truth: $147.67 Error: $5.61 SLE: 0.00 Item: Headlight Assembly C...\u001b[0m\n",
            "\u001b[92m23: Guess: $33.37 Truth: $24.99 Error: $8.38 SLE: 0.08 Item: ASI NAUTICAL 2.5 Inc...\u001b[0m\n",
            "\u001b[92m24: Guess: $121.59 Truth: $149.00 Error: $27.41 SLE: 0.04 Item: Behringer TUBE OVERD...\u001b[0m\n",
            "\u001b[92m25: Guess: $6.98 Truth: $16.99 Error: $10.01 SLE: 0.66 Item: Fun Express Insect F...\u001b[0m\n",
            "\u001b[92m26: Guess: $6.39 Truth: $7.99 Error: $1.60 SLE: 0.04 Item: WAFJAMF Roller Stamp...\u001b[0m\n",
            "\u001b[91m27: Guess: $295.14 Truth: $199.99 Error: $95.15 SLE: 0.15 Item: Capulina Tiffany Flo...\u001b[0m\n",
            "\u001b[92m28: Guess: $203.96 Truth: $251.45 Error: $47.49 SLE: 0.04 Item: Apple Watch Series 6...\u001b[0m\n",
            "\u001b[91m29: Guess: $94.37 Truth: $231.62 Error: $137.25 SLE: 0.80 Item: ICON 01725 Tandem Ax...\u001b[0m\n",
            "\u001b[93m30: Guess: $79.71 Truth: $135.00 Error: $55.29 SLE: 0.27 Item: SanDisk 128GB Ultra ...\u001b[0m\n",
            "\u001b[92m31: Guess: $349.89 Truth: $356.62 Error: $6.73 SLE: 0.00 Item: Velvac - 715427\n",
            "2020...\u001b[0m\n",
            "\u001b[92m32: Guess: $302.03 Truth: $257.99 Error: $44.04 SLE: 0.02 Item: TCMT Passenger Backr...\u001b[0m\n",
            "\u001b[92m33: Guess: $35.45 Truth: $27.99 Error: $7.46 SLE: 0.05 Item: Alnicov 63.5MM Brass...\u001b[0m\n",
            "\u001b[92m34: Guess: $149.21 Truth: $171.20 Error: $21.99 SLE: 0.02 Item: Subaru Forester Outb...\u001b[0m\n",
            "\u001b[92m35: Guess: $197.44 Truth: $225.00 Error: $27.56 SLE: 0.02 Item: Richmond Auto Uphols...\u001b[0m\n",
            "\u001b[92m36: Guess: $79.59 Truth: $105.00 Error: $25.41 SLE: 0.08 Item: AP-39 Automotive Pai...\u001b[0m\n",
            "\u001b[92m37: Guess: $269.15 Truth: $299.99 Error: $30.84 SLE: 0.01 Item: Road Top Wireless Ca...\u001b[0m\n",
            "\u001b[92m38: Guess: $592.03 Truth: $535.09 Error: $56.94 SLE: 0.01 Item: Gibson Performance E...\u001b[0m\n",
            "\u001b[92m39: Guess: $14.33 Truth: $12.33 Error: $2.00 SLE: 0.02 Item: Bella Tunno Happy Li...\u001b[0m\n",
            "\u001b[92m40: Guess: $103.11 Truth: $84.99 Error: $18.12 SLE: 0.04 Item: CANMORE H300 Handhel...\u001b[0m\n",
            "\u001b[92m41: Guess: $31.57 Truth: $15.99 Error: $15.58 SLE: 0.42 Item: DCPOWER AC Adapter C...\u001b[0m\n",
            "\u001b[92m42: Guess: $35.72 Truth: $62.44 Error: $26.72 SLE: 0.30 Item: Sharp, Commercial De...\u001b[0m\n",
            "\u001b[92m43: Guess: $70.23 Truth: $82.99 Error: $12.76 SLE: 0.03 Item: Melissa & Doug Lifel...\u001b[0m\n",
            "\u001b[91m44: Guess: $315.71 Truth: $599.95 Error: $284.24 SLE: 0.41 Item: Sony SSCS8 2-Way Cen...\u001b[0m\n",
            "\u001b[93m45: Guess: $268.09 Truth: $194.99 Error: $73.10 SLE: 0.10 Item: ASUS Chromebook CX1,...\u001b[0m\n",
            "\u001b[92m46: Guess: $312.44 Truth: $344.95 Error: $32.51 SLE: 0.01 Item: FiiO X7 32GB Hi-Res ...\u001b[0m\n",
            "\u001b[92m47: Guess: $28.33 Truth: $37.99 Error: $9.66 SLE: 0.08 Item: TORRO Leather Case C...\u001b[0m\n",
            "\u001b[92m48: Guess: $226.03 Truth: $224.35 Error: $1.68 SLE: 0.00 Item: Universal Air Condit...\u001b[0m\n",
            "\u001b[93m49: Guess: $497.58 Truth: $814.00 Error: $316.42 SLE: 0.24 Item: Street Series Stainl...\u001b[0m\n",
            "\u001b[92m50: Guess: $380.76 Truth: $439.88 Error: $59.12 SLE: 0.02 Item: Lenovo IdeaPad 3 Lap...\u001b[0m\n",
            "\u001b[92m51: Guess: $301.64 Truth: $341.43 Error: $39.79 SLE: 0.02 Item: Access Bed Covers To...\u001b[0m\n",
            "\u001b[92m52: Guess: $60.71 Truth: $46.78 Error: $13.93 SLE: 0.07 Item: G.I. JOE Hasbro 3 3/...\u001b[0m\n",
            "\u001b[92m53: Guess: $153.47 Truth: $171.44 Error: $17.97 SLE: 0.01 Item: T&S Brass Double Pan...\u001b[0m\n",
            "\u001b[91m54: Guess: $159.75 Truth: $458.00 Error: $298.25 SLE: 1.10 Item: ZTUOAUMA Fuel Inject...\u001b[0m\n",
            "\u001b[91m55: Guess: $282.84 Truth: $130.75 Error: $152.09 SLE: 0.59 Item: Hp Prime Graphing Ca...\u001b[0m\n",
            "\u001b[92m56: Guess: $58.77 Truth: $83.81 Error: $25.04 SLE: 0.12 Item: Lowrance Nmea 2000 2...\u001b[0m\n",
            "\u001b[91m57: Guess: $152.93 Truth: $386.39 Error: $233.46 SLE: 0.85 Item: Jeep Genuine Accesso...\u001b[0m\n",
            "\u001b[91m58: Guess: $266.77 Truth: $169.00 Error: $97.77 SLE: 0.21 Item: GODOX CB-06 Hard Car...\u001b[0m\n",
            "\u001b[92m59: Guess: $6.97 Truth: $17.95 Error: $10.98 SLE: 0.75 Item: Au-Tomotive Gold, IN...\u001b[0m\n",
            "\u001b[92m60: Guess: $263.65 Truth: $269.00 Error: $5.35 SLE: 0.00 Item: Snailfly Black Roof ...\u001b[0m\n",
            "\u001b[92m61: Guess: $66.15 Truth: $77.77 Error: $11.62 SLE: 0.03 Item: KING SHA Anti Glare ...\u001b[0m\n",
            "\u001b[93m62: Guess: $149.63 Truth: $88.99 Error: $60.64 SLE: 0.27 Item: APS Compatible with ...\u001b[0m\n",
            "\u001b[92m63: Guess: $310.10 Truth: $364.41 Error: $54.31 SLE: 0.03 Item: Wilwood Engineering ...\u001b[0m\n",
            "\u001b[92m64: Guess: $152.31 Truth: $127.03 Error: $25.28 SLE: 0.03 Item: ACDelco Gold Starter...\u001b[0m\n",
            "\u001b[93m65: Guess: $499.48 Truth: $778.95 Error: $279.47 SLE: 0.20 Item: UWS Matte Black Heav...\u001b[0m\n",
            "\u001b[93m66: Guess: $265.52 Truth: $206.66 Error: $58.86 SLE: 0.06 Item: Dell Latitude E5440 ...\u001b[0m\n",
            "\u001b[92m67: Guess: $58.97 Truth: $35.94 Error: $23.03 SLE: 0.23 Item: (Plug and Play) Spar...\u001b[0m\n",
            "\u001b[91m68: Guess: $293.95 Truth: $149.00 Error: $144.95 SLE: 0.46 Item: The Ultimate Roadsid...\u001b[0m\n",
            "\u001b[93m69: Guess: $177.24 Truth: $251.98 Error: $74.74 SLE: 0.12 Item: Brand New 18 x 8.5 R...\u001b[0m\n",
            "\u001b[92m70: Guess: $173.46 Truth: $160.00 Error: $13.46 SLE: 0.01 Item: Headlight Headlamp L...\u001b[0m\n",
            "\u001b[92m71: Guess: $59.86 Truth: $39.99 Error: $19.87 SLE: 0.16 Item: Lilo And Stitch Delu...\u001b[0m\n",
            "\u001b[91m72: Guess: $211.91 Truth: $362.41 Error: $150.50 SLE: 0.29 Item: AC Compressor & A/C ...\u001b[0m\n",
            "\u001b[91m73: Guess: $186.49 Truth: $344.00 Error: $157.51 SLE: 0.37 Item: House Of Troy Pinnac...\u001b[0m\n",
            "\u001b[92m74: Guess: $6.38 Truth: $25.09 Error: $18.71 SLE: 1.59 Item: Juno T29 WH Floating...\u001b[0m\n",
            "\u001b[93m75: Guess: $126.64 Truth: $175.95 Error: $49.31 SLE: 0.11 Item: Sherman GO-PARTS - f...\u001b[0m\n",
            "\u001b[92m76: Guess: $115.73 Truth: $132.64 Error: $16.91 SLE: 0.02 Item: Roland RPU-3 Electro...\u001b[0m\n",
            "\u001b[92m77: Guess: $420.00 Truth: $422.99 Error: $2.99 SLE: 0.00 Item: Rockland VMI14 12,00...\u001b[0m\n",
            "\u001b[92m78: Guess: $155.96 Truth: $146.48 Error: $9.48 SLE: 0.00 Item: Max Advanced Brakes ...\u001b[0m\n",
            "\u001b[92m79: Guess: $165.06 Truth: $156.83 Error: $8.23 SLE: 0.00 Item: Quality-Built 11030 ...\u001b[0m\n",
            "\u001b[91m80: Guess: $132.64 Truth: $251.99 Error: $119.35 SLE: 0.41 Item: Lucida LG-510 Studen...\u001b[0m\n",
            "\u001b[91m81: Guess: $229.81 Truth: $940.33 Error: $710.52 SLE: 1.98 Item: Longacre Aluminum Tu...\u001b[0m\n",
            "\u001b[92m82: Guess: $31.24 Truth: $52.99 Error: $21.75 SLE: 0.27 Item: Motion Pro Adjustabl...\u001b[0m\n",
            "\u001b[91m83: Guess: $101.22 Truth: $219.95 Error: $118.73 SLE: 0.59 Item: Glyph Thunderbolt 3 ...\u001b[0m\n",
            "\u001b[91m84: Guess: $205.89 Truth: $441.03 Error: $235.14 SLE: 0.58 Item: TOYO Open Country MT...\u001b[0m\n",
            "\u001b[91m85: Guess: $281.51 Truth: $168.98 Error: $112.53 SLE: 0.26 Item: Razer Seiren X USB S...\u001b[0m\n",
            "\u001b[92m86: Guess: $4.00 Truth: $2.49 Error: $1.51 SLE: 0.13 Item: Happy Birthday to Da...\u001b[0m\n",
            "\u001b[92m87: Guess: $80.43 Truth: $98.62 Error: $18.19 SLE: 0.04 Item: Little Tikes My Real...\u001b[0m\n",
            "\u001b[92m88: Guess: $265.53 Truth: $256.95 Error: $8.58 SLE: 0.00 Item: Studio M Peace and H...\u001b[0m\n",
            "\u001b[92m89: Guess: $21.91 Truth: $30.99 Error: $9.08 SLE: 0.11 Item: MyVolts 12V Power Su...\u001b[0m\n",
            "\u001b[91m90: Guess: $283.25 Truth: $569.84 Error: $286.59 SLE: 0.49 Item: Dell Latitude 7212 R...\u001b[0m\n",
            "\u001b[91m91: Guess: $265.25 Truth: $177.99 Error: $87.26 SLE: 0.16 Item: Covermates Contour F...\u001b[0m\n",
            "\u001b[91m92: Guess: $415.89 Truth: $997.99 Error: $582.10 SLE: 0.76 Item: Westin Black HDX Gri...\u001b[0m\n",
            "\u001b[93m93: Guess: $284.59 Truth: $219.00 Error: $65.59 SLE: 0.07 Item: Fieldpiece JL2 Job L...\u001b[0m\n",
            "\u001b[91m94: Guess: $345.89 Truth: $225.55 Error: $120.34 SLE: 0.18 Item: hansgrohe Talis S Mo...\u001b[0m\n",
            "\u001b[93m95: Guess: $365.28 Truth: $495.95 Error: $130.67 SLE: 0.09 Item: G-Technology G-SPEED...\u001b[0m\n",
            "\u001b[92m96: Guess: $805.84 Truth: $942.37 Error: $136.53 SLE: 0.02 Item: DreamLine Shower Doo...\u001b[0m\n",
            "\u001b[92m97: Guess: $35.34 Truth: $1.94 Error: $33.40 SLE: 6.32 Item: Sanctuary Square Bac...\u001b[0m\n",
            "\u001b[92m98: Guess: $299.93 Truth: $284.34 Error: $15.59 SLE: 0.00 Item: Pelican Protector 17...\u001b[0m\n",
            "\u001b[92m99: Guess: $169.41 Truth: $171.90 Error: $2.49 SLE: 0.00 Item: Brock Replacement Dr...\u001b[0m\n",
            "\u001b[92m100: Guess: $120.55 Truth: $144.99 Error: $24.44 SLE: 0.03 Item: Carlinkit Ai Box Min...\u001b[0m\n",
            "\u001b[91m101: Guess: $172.95 Truth: $470.47 Error: $297.52 SLE: 0.99 Item: StarDot YouTube Live...\u001b[0m\n",
            "\u001b[92m102: Guess: $43.73 Truth: $66.95 Error: $23.22 SLE: 0.17 Item: Atomic Compatible ME...\u001b[0m\n",
            "\u001b[93m103: Guess: $61.82 Truth: $117.00 Error: $55.18 SLE: 0.40 Item: Bandai Awakening of ...\u001b[0m\n",
            "\u001b[91m104: Guess: $346.40 Truth: $172.14 Error: $174.26 SLE: 0.48 Item: Fit System 62135G Pa...\u001b[0m\n",
            "\u001b[93m105: Guess: $278.95 Truth: $392.74 Error: $113.79 SLE: 0.12 Item: Black Horse Black Al...\u001b[0m\n",
            "\u001b[92m106: Guess: $17.00 Truth: $16.99 Error: $0.01 SLE: 0.00 Item: Dearsun Twinkle Star...\u001b[0m\n",
            "\u001b[92m107: Guess: $1.80 Truth: $1.34 Error: $0.46 SLE: 0.03 Item: Pokemon - Gallade Sp...\u001b[0m\n",
            "\u001b[92m108: Guess: $368.96 Truth: $349.98 Error: $18.98 SLE: 0.00 Item: Ibanez GIO Series Cl...\u001b[0m\n",
            "\u001b[91m109: Guess: $176.85 Truth: $370.71 Error: $193.86 SLE: 0.54 Item: Set 2 Heavy Duty 12 ...\u001b[0m\n",
            "\u001b[92m110: Guess: $39.20 Truth: $65.88 Error: $26.68 SLE: 0.26 Item: Hairpin Table Legs 2...\u001b[0m\n",
            "\u001b[92m111: Guess: $267.37 Truth: $229.99 Error: $37.38 SLE: 0.02 Item: Marada Racing Seat w...\u001b[0m\n",
            "\u001b[92m112: Guess: $4.98 Truth: $9.14 Error: $4.16 SLE: 0.28 Item: Remington Industries...\u001b[0m\n",
            "\u001b[91m113: Guess: $431.29 Truth: $199.00 Error: $232.29 SLE: 0.59 Item: Acer Ultrabook, Inte...\u001b[0m\n",
            "\u001b[91m114: Guess: $297.35 Truth: $109.99 Error: $187.36 SLE: 0.98 Item: ICBEAMER 7 RGB LED H...\u001b[0m\n",
            "\u001b[93m115: Guess: $419.58 Truth: $570.42 Error: $150.84 SLE: 0.09 Item: R1 Concepts Front Re...\u001b[0m\n",
            "\u001b[92m116: Guess: $301.63 Truth: $279.99 Error: $21.64 SLE: 0.01 Item: Camplux 2.64 GPM Tan...\u001b[0m\n",
            "\u001b[92m117: Guess: $21.97 Truth: $30.99 Error: $9.02 SLE: 0.11 Item: KNOKLOCK 10 Pack 3.7...\u001b[0m\n",
            "\u001b[92m118: Guess: $33.28 Truth: $31.99 Error: $1.29 SLE: 0.00 Item: Valley Enterprises Y...\u001b[0m\n",
            "\u001b[92m119: Guess: $30.53 Truth: $15.90 Error: $14.63 SLE: 0.39 Item: G9 LED Light 100W re...\u001b[0m\n",
            "\u001b[92m120: Guess: $82.88 Truth: $45.99 Error: $36.89 SLE: 0.34 Item: ZCHAOZ 4 Lights Anti...\u001b[0m\n",
            "\u001b[93m121: Guess: $171.25 Truth: $113.52 Error: $57.73 SLE: 0.17 Item: Honeywell Honeywell ...\u001b[0m\n",
            "\u001b[91m122: Guess: $277.90 Truth: $516.99 Error: $239.09 SLE: 0.38 Item: Patriot Exhaust 1-7/...\u001b[0m\n",
            "\u001b[92m123: Guess: $162.09 Truth: $196.99 Error: $34.90 SLE: 0.04 Item: Fitrite Autopart New...\u001b[0m\n",
            "\u001b[92m124: Guess: $36.59 Truth: $46.55 Error: $9.96 SLE: 0.06 Item: Technical Precision ...\u001b[0m\n",
            "\u001b[93m125: Guess: $277.80 Truth: $356.99 Error: $79.19 SLE: 0.06 Item: Covercraft Carhartt ...\u001b[0m\n",
            "\u001b[92m126: Guess: $284.98 Truth: $319.95 Error: $34.97 SLE: 0.01 Item: Sennheiser SD Pro 2 ...\u001b[0m\n",
            "\u001b[92m127: Guess: $91.80 Truth: $96.06 Error: $4.26 SLE: 0.00 Item: Hitachi Mass Air Flo...\u001b[0m\n",
            "\u001b[91m128: Guess: $300.05 Truth: $190.99 Error: $109.06 SLE: 0.20 Item: AmScope LED Cordless...\u001b[0m\n",
            "\u001b[91m129: Guess: $64.53 Truth: $257.95 Error: $193.42 SLE: 1.89 Item: Front Left Driver Si...\u001b[0m\n",
            "\u001b[92m130: Guess: $72.01 Truth: $62.95 Error: $9.06 SLE: 0.02 Item: Premium Replica Hubc...\u001b[0m\n",
            "\u001b[92m131: Guess: $26.06 Truth: $47.66 Error: $21.60 SLE: 0.34 Item: Excellerations Phoni...\u001b[0m\n",
            "\u001b[93m132: Guess: $298.04 Truth: $226.99 Error: $71.05 SLE: 0.07 Item: RC4WD BigDog Dual Ax...\u001b[0m\n",
            "\u001b[93m133: Guess: $276.46 Truth: $359.95 Error: $83.49 SLE: 0.07 Item: Unknown Stage 2 Clut...\u001b[0m\n",
            "\u001b[92m134: Guess: $75.69 Truth: $78.40 Error: $2.71 SLE: 0.00 Item: Dodge Ram 1500 Mopar...\u001b[0m\n",
            "\u001b[92m135: Guess: $202.54 Truth: $172.77 Error: $29.77 SLE: 0.03 Item: Pro Comp Alloys Seri...\u001b[0m\n",
            "\u001b[91m136: Guess: $187.00 Truth: $316.45 Error: $129.45 SLE: 0.27 Item: Detroit Axle - Front...\u001b[0m\n",
            "\u001b[93m137: Guess: $153.18 Truth: $87.99 Error: $65.19 SLE: 0.30 Item: ECCPP Rear Wheel Axl...\u001b[0m\n",
            "\u001b[93m138: Guess: $139.05 Truth: $226.63 Error: $87.58 SLE: 0.24 Item: Dell Latitude E6520 ...\u001b[0m\n",
            "\u001b[93m139: Guess: $79.72 Truth: $31.49 Error: $48.23 SLE: 0.83 Item: F FIERCE CYCLE 251pc...\u001b[0m\n",
            "\u001b[93m140: Guess: $125.46 Truth: $196.00 Error: $70.54 SLE: 0.20 Item: Flash Furniture 4 Pk...\u001b[0m\n",
            "\u001b[92m141: Guess: $54.15 Truth: $78.40 Error: $24.25 SLE: 0.13 Item: B&M 30287 Throttle V...\u001b[0m\n",
            "\u001b[92m142: Guess: $134.92 Truth: $116.25 Error: $18.67 SLE: 0.02 Item: Gates TCK226 PowerGr...\u001b[0m\n",
            "\u001b[93m143: Guess: $153.39 Truth: $112.78 Error: $40.61 SLE: 0.09 Item: Monroe Shocks & Stru...\u001b[0m\n",
            "\u001b[92m144: Guess: $66.37 Truth: $27.32 Error: $39.05 SLE: 0.75 Item: Feit Electric 35W EQ...\u001b[0m\n",
            "\u001b[91m145: Guess: $65.11 Truth: $145.91 Error: $80.80 SLE: 0.64 Item: Yellow Jacket 2806 C...\u001b[0m\n",
            "\u001b[92m146: Guess: $159.63 Truth: $171.09 Error: $11.46 SLE: 0.00 Item: Garage-Pro Tailgate ...\u001b[0m\n",
            "\u001b[91m147: Guess: $79.70 Truth: $167.95 Error: $88.25 SLE: 0.55 Item: 3M Perfect It Buffin...\u001b[0m\n",
            "\u001b[92m148: Guess: $39.46 Truth: $28.49 Error: $10.97 SLE: 0.10 Item: Chinese Style Dollho...\u001b[0m\n",
            "\u001b[93m149: Guess: $44.17 Truth: $122.23 Error: $78.06 SLE: 1.01 Item: Generic NRG Innovati...\u001b[0m\n",
            "\u001b[92m150: Guess: $27.17 Truth: $32.99 Error: $5.82 SLE: 0.04 Item: Learning Resources C...\u001b[0m\n",
            "\u001b[92m151: Guess: $50.77 Truth: $71.20 Error: $20.43 SLE: 0.11 Item: Bosch Automotive 154...\u001b[0m\n",
            "\u001b[93m152: Guess: $51.80 Truth: $112.75 Error: $60.95 SLE: 0.59 Item: Case of 24-2 Inch Bl...\u001b[0m\n",
            "\u001b[92m153: Guess: $153.37 Truth: $142.43 Error: $10.94 SLE: 0.01 Item: MOCA Engine Water Pu...\u001b[0m\n",
            "\u001b[92m154: Guess: $324.18 Truth: $398.99 Error: $74.81 SLE: 0.04 Item: SAREMAS Foot Step Ba...\u001b[0m\n",
            "\u001b[92m155: Guess: $430.54 Truth: $449.00 Error: $18.46 SLE: 0.00 Item: Gretsch G9210 Square...\u001b[0m\n",
            "\u001b[91m156: Guess: $69.76 Truth: $189.00 Error: $119.24 SLE: 0.98 Item: NikoMaku Mirror Dash...\u001b[0m\n",
            "\u001b[92m157: Guess: $103.88 Truth: $120.91 Error: $17.03 SLE: 0.02 Item: Fenix HP25R v2.0 USB...\u001b[0m\n",
            "\u001b[91m158: Guess: $342.34 Truth: $203.53 Error: $138.81 SLE: 0.27 Item: R&L Racing Heavy Dut...\u001b[0m\n",
            "\u001b[92m159: Guess: $323.96 Truth: $349.99 Error: $26.03 SLE: 0.01 Item: Garmin GPSMAP 64sx, ...\u001b[0m\n",
            "\u001b[92m160: Guess: $7.02 Truth: $34.35 Error: $27.33 SLE: 2.20 Item: Brown 5-7/8 X 8-1/2 ...\u001b[0m\n",
            "\u001b[93m161: Guess: $231.24 Truth: $384.99 Error: $153.75 SLE: 0.26 Item: GAOMON PD2200 Pen Di...\u001b[0m\n",
            "\u001b[92m162: Guess: $176.94 Truth: $211.00 Error: $34.06 SLE: 0.03 Item: VXMOTOR for 97-03 Fo...\u001b[0m\n",
            "\u001b[92m163: Guess: $152.69 Truth: $129.00 Error: $23.69 SLE: 0.03 Item: HP EliteBook 2540p I...\u001b[0m\n",
            "\u001b[93m164: Guess: $63.83 Truth: $111.45 Error: $47.62 SLE: 0.30 Item: Green EPX Mixing Noz...\u001b[0m\n",
            "\u001b[93m165: Guess: $6.37 Truth: $81.12 Error: $74.75 SLE: 5.81 Item: Box Partners 6 1/4 x...\u001b[0m\n",
            "\u001b[91m166: Guess: $241.26 Truth: $457.08 Error: $215.82 SLE: 0.41 Item: Vixen Air 1/2 NPT Ai...\u001b[0m\n",
            "\u001b[92m167: Guess: $69.97 Truth: $49.49 Error: $20.48 SLE: 0.12 Item: Smart Floor Lamp, Mu...\u001b[0m\n",
            "\u001b[93m168: Guess: $35.42 Truth: $80.56 Error: $45.14 SLE: 0.65 Item: SOZG 324mm Wheelbase...\u001b[0m\n",
            "\u001b[92m169: Guess: $233.27 Truth: $278.39 Error: $45.12 SLE: 0.03 Item: Mickey Thompson ET S...\u001b[0m\n",
            "\u001b[91m170: Guess: $205.78 Truth: $364.50 Error: $158.72 SLE: 0.32 Item: Pirelli 106W XL RFT ...\u001b[0m\n",
            "\u001b[91m171: Guess: $157.05 Truth: $378.99 Error: $221.94 SLE: 0.77 Item: Torklift C3212 Rear ...\u001b[0m\n",
            "\u001b[92m172: Guess: $157.14 Truth: $165.28 Error: $8.14 SLE: 0.00 Item: Cardone Remanufactur...\u001b[0m\n",
            "\u001b[92m173: Guess: $85.15 Truth: $56.74 Error: $28.41 SLE: 0.16 Item: Kidde AccessPoint 00...\u001b[0m\n",
            "\u001b[91m174: Guess: $154.30 Truth: $307.95 Error: $153.65 SLE: 0.47 Item: 3M Protecta Self Ret...\u001b[0m\n",
            "\u001b[93m175: Guess: $105.67 Truth: $38.00 Error: $67.67 SLE: 1.01 Item: Plantronics Wired He...\u001b[0m\n",
            "\u001b[92m176: Guess: $83.22 Truth: $53.00 Error: $30.22 SLE: 0.20 Item: Logitech K750 Wirele...\u001b[0m\n",
            "\u001b[92m177: Guess: $399.80 Truth: $498.00 Error: $98.20 SLE: 0.05 Item: Olympus PEN E-PL9 Bo...\u001b[0m\n",
            "\u001b[93m178: Guess: $116.73 Truth: $53.99 Error: $62.74 SLE: 0.58 Item: Beck/Arnley Hub & Be...\u001b[0m\n",
            "\u001b[93m179: Guess: $256.49 Truth: $350.00 Error: $93.51 SLE: 0.10 Item: Eibach Pro-Kit Perfo...\u001b[0m\n",
            "\u001b[92m180: Guess: $245.37 Truth: $299.95 Error: $54.58 SLE: 0.04 Item: LEGO DC Batman 1989 ...\u001b[0m\n",
            "\u001b[92m181: Guess: $79.17 Truth: $94.93 Error: $15.76 SLE: 0.03 Item: Kingston Brass Resto...\u001b[0m\n",
            "\u001b[93m182: Guess: $296.25 Truth: $379.00 Error: $82.75 SLE: 0.06 Item: Polk Vanishing Serie...\u001b[0m\n",
            "\u001b[92m183: Guess: $263.51 Truth: $299.95 Error: $36.44 SLE: 0.02 Item: Spec-D Tuning LED Pr...\u001b[0m\n",
            "\u001b[92m184: Guess: $17.38 Truth: $24.99 Error: $7.61 SLE: 0.12 Item: RICHMOND & FINCH Air...\u001b[0m\n",
            "\u001b[92m185: Guess: $79.31 Truth: $41.04 Error: $38.27 SLE: 0.42 Item: LFA Industries - mm ...\u001b[0m\n",
            "\u001b[93m186: Guess: $231.44 Truth: $327.90 Error: $96.46 SLE: 0.12 Item: SAUTVS LED Headlight...\u001b[0m\n",
            "\u001b[92m187: Guess: $22.19 Truth: $10.99 Error: $11.20 SLE: 0.44 Item: 2 Pack Combo Womens ...\u001b[0m\n",
            "\u001b[92m188: Guess: $14.64 Truth: $14.99 Error: $0.35 SLE: 0.00 Item: Arepa - Venezuelan c...\u001b[0m\n",
            "\u001b[91m189: Guess: $4.32 Truth: $84.95 Error: $80.63 SLE: 7.74 Item: Schlage Lock Company...\u001b[0m\n",
            "\u001b[92m190: Guess: $116.79 Truth: $111.00 Error: $5.79 SLE: 0.00 Item: Techni Mobili White ...\u001b[0m\n",
            "\u001b[92m191: Guess: $148.28 Truth: $123.73 Error: $24.55 SLE: 0.03 Item: Special Lite Product...\u001b[0m\n",
            "\u001b[91m192: Guess: $121.38 Truth: $557.38 Error: $436.00 SLE: 2.30 Item: Tascam Digital Porta...\u001b[0m\n",
            "\u001b[92m193: Guess: $116.17 Truth: $95.55 Error: $20.62 SLE: 0.04 Item: Glow Lighting Vista ...\u001b[0m\n",
            "\u001b[91m194: Guess: $264.14 Truth: $154.00 Error: $110.14 SLE: 0.29 Item: Z3 Wind Deflector, S...\u001b[0m\n",
            "\u001b[91m195: Guess: $284.63 Truth: $198.99 Error: $85.64 SLE: 0.13 Item: Olympus E-20 5MP Dig...\u001b[0m\n",
            "\u001b[91m196: Guess: $160.59 Truth: $430.44 Error: $269.85 SLE: 0.96 Item: PHYNEDI 1 1000 World...\u001b[0m\n",
            "\u001b[92m197: Guess: $19.63 Truth: $45.67 Error: $26.04 SLE: 0.67 Item: YANGHUAN Unstable Un...\u001b[0m\n",
            "\u001b[92m198: Guess: $276.30 Truth: $249.00 Error: $27.30 SLE: 0.01 Item: Interlogix NetworX T...\u001b[0m\n",
            "\u001b[92m199: Guess: $18.64 Truth: $42.99 Error: $24.35 SLE: 0.65 Item: Steering Damper,Univ...\u001b[0m\n",
            "\u001b[91m200: Guess: $74.41 Truth: $181.33 Error: $106.92 SLE: 0.78 Item: Amprobe TIC 410A Hot...\u001b[0m\n",
            "\u001b[92m201: Guess: $2.89 Truth: $6.03 Error: $3.14 SLE: 0.35 Item: MyCableMart 3.5mm Pl...\u001b[0m\n",
            "\u001b[92m202: Guess: $30.60 Truth: $29.99 Error: $0.61 SLE: 0.00 Item: OtterBox + Pop Symme...\u001b[0m\n",
            "\u001b[91m203: Guess: $530.54 Truth: $899.00 Error: $368.46 SLE: 0.28 Item: Dell XPS Desktop ( I...\u001b[0m\n",
            "\u001b[92m204: Guess: $344.22 Truth: $399.99 Error: $55.77 SLE: 0.02 Item: Franklin Iron Works ...\u001b[0m\n",
            "\u001b[93m205: Guess: $48.20 Truth: $4.66 Error: $43.54 SLE: 4.68 Item: Avery Legal Dividers...\u001b[0m\n",
            "\u001b[92m206: Guess: $277.24 Truth: $261.41 Error: $15.83 SLE: 0.00 Item: Moen 8346 Commercial...\u001b[0m\n",
            "\u001b[92m207: Guess: $173.08 Truth: $136.97 Error: $36.11 SLE: 0.05 Item: Carlisle Versa Trail...\u001b[0m\n",
            "\u001b[93m208: Guess: $122.44 Truth: $79.00 Error: $43.44 SLE: 0.19 Item: SUNWAYFOTO 44mm Trip...\u001b[0m\n",
            "\u001b[91m209: Guess: $112.88 Truth: $444.99 Error: $332.11 SLE: 1.86 Item: NanoBeam AC 4 Units ...\u001b[0m\n",
            "\u001b[92m210: Guess: $347.14 Truth: $411.94 Error: $64.80 SLE: 0.03 Item: WULF 4 Front 2 Rear ...\u001b[0m\n",
            "\u001b[92m211: Guess: $144.47 Truth: $148.40 Error: $3.93 SLE: 0.00 Item: Alera ALEVABFMC Vale...\u001b[0m\n",
            "\u001b[91m212: Guess: $81.68 Truth: $244.99 Error: $163.31 SLE: 1.19 Item: YU-GI-OH! Ignition A...\u001b[0m\n",
            "\u001b[92m213: Guess: $63.75 Truth: $86.50 Error: $22.75 SLE: 0.09 Item: 48 x 36 Extra-Large ...\u001b[0m\n",
            "\u001b[92m214: Guess: $265.89 Truth: $297.95 Error: $32.06 SLE: 0.01 Item: Dell Latitude D620 R...\u001b[0m\n",
            "\u001b[91m215: Guess: $593.73 Truth: $399.99 Error: $193.74 SLE: 0.16 Item: acer Aspire 5 Laptop...\u001b[0m\n",
            "\u001b[91m216: Guess: $154.09 Truth: $599.00 Error: $444.91 SLE: 1.83 Item: Elk 30 by 6-Inch Viv...\u001b[0m\n",
            "\u001b[92m217: Guess: $73.24 Truth: $105.99 Error: $32.75 SLE: 0.13 Item: Barbie Top Model Dol...\u001b[0m\n",
            "\u001b[91m218: Guess: $351.53 Truth: $689.00 Error: $337.47 SLE: 0.45 Item: Danby Designer 20-In...\u001b[0m\n",
            "\u001b[92m219: Guess: $345.68 Truth: $404.99 Error: $59.31 SLE: 0.02 Item: FixtureDisplays® Met...\u001b[0m\n",
            "\u001b[92m220: Guess: $184.54 Truth: $207.76 Error: $23.22 SLE: 0.01 Item: ACDelco GM Original ...\u001b[0m\n",
            "\u001b[91m221: Guess: $283.70 Truth: $171.82 Error: $111.88 SLE: 0.25 Item: EBC Premium Street B...\u001b[0m\n",
            "\u001b[92m222: Guess: $349.86 Truth: $293.24 Error: $56.62 SLE: 0.03 Item: FXR Men's Boost FX J...\u001b[0m\n",
            "\u001b[92m223: Guess: $300.19 Truth: $374.95 Error: $74.76 SLE: 0.05 Item: SuperATV Scratch Res...\u001b[0m\n",
            "\u001b[92m224: Guess: $143.71 Truth: $111.99 Error: $31.72 SLE: 0.06 Item: SBU 3 Layer All Weat...\u001b[0m\n",
            "\u001b[92m225: Guess: $18.61 Truth: $42.99 Error: $24.38 SLE: 0.65 Item: 2 Pack Outdoor Broch...\u001b[0m\n",
            "\u001b[92m226: Guess: $153.36 Truth: $116.71 Error: $36.65 SLE: 0.07 Item: Monroe Shocks & Stru...\u001b[0m\n",
            "\u001b[93m227: Guess: $174.01 Truth: $118.61 Error: $55.40 SLE: 0.14 Item: Elements of Design M...\u001b[0m\n",
            "\u001b[92m228: Guess: $162.07 Truth: $147.12 Error: $14.95 SLE: 0.01 Item: GM Genuine Parts Air...\u001b[0m\n",
            "\u001b[92m229: Guess: $120.22 Truth: $119.99 Error: $0.23 SLE: 0.00 Item: Baseus USB C Docking...\u001b[0m\n",
            "\u001b[93m230: Guess: $263.76 Truth: $369.98 Error: $106.22 SLE: 0.11 Item: Whitehall™ Personali...\u001b[0m\n",
            "\u001b[93m231: Guess: $239.00 Truth: $315.55 Error: $76.55 SLE: 0.08 Item: Pro Circuit Works Pi...\u001b[0m\n",
            "\u001b[91m232: Guess: $294.48 Truth: $190.99 Error: $103.49 SLE: 0.19 Item: HYANKA 15 1200W Prof...\u001b[0m\n",
            "\u001b[91m233: Guess: $52.85 Truth: $155.00 Error: $102.15 SLE: 1.13 Item: Bluetooth X6BT Card ...\u001b[0m\n",
            "\u001b[92m234: Guess: $301.84 Truth: $349.99 Error: $48.15 SLE: 0.02 Item: AIRAID Cold Air Inta...\u001b[0m\n",
            "\u001b[92m235: Guess: $297.89 Truth: $249.99 Error: $47.90 SLE: 0.03 Item: Bostingner Shower Fa...\u001b[0m\n",
            "\u001b[92m236: Guess: $45.34 Truth: $42.99 Error: $2.35 SLE: 0.00 Item: PIT66 Front Bumper T...\u001b[0m\n",
            "\u001b[92m237: Guess: $13.65 Truth: $17.99 Error: $4.34 SLE: 0.07 Item: Caseology Bumpy Comp...\u001b[0m\n",
            "\u001b[93m238: Guess: $276.88 Truth: $425.00 Error: $148.12 SLE: 0.18 Item: Fleck 2510 Timer Mec...\u001b[0m\n",
            "\u001b[92m239: Guess: $279.23 Truth: $249.99 Error: $29.24 SLE: 0.01 Item: Haloview MC7108 Wire...\u001b[0m\n",
            "\u001b[91m240: Guess: $34.33 Truth: $138.23 Error: $103.90 SLE: 1.88 Item: Schmidt Spiele - Man...\u001b[0m\n",
            "\u001b[93m241: Guess: $300.62 Truth: $414.99 Error: $114.37 SLE: 0.10 Item: Corsa 14333 Tip Kit ...\u001b[0m\n",
            "\u001b[91m242: Guess: $85.28 Truth: $168.28 Error: $83.00 SLE: 0.45 Item: Hoshizaki FM116A Fan...\u001b[0m\n",
            "\u001b[93m243: Guess: $269.89 Truth: $199.99 Error: $69.90 SLE: 0.09 Item: BAINUO Antler Chande...\u001b[0m\n",
            "\u001b[92m244: Guess: $159.50 Truth: $126.70 Error: $32.80 SLE: 0.05 Item: DNA MOTORING Smoke L...\u001b[0m\n",
            "\u001b[92m245: Guess: $5.00 Truth: $5.91 Error: $0.91 SLE: 0.02 Item: Wera Stainless 3840/...\u001b[0m\n",
            "\u001b[91m246: Guess: $300.34 Truth: $193.06 Error: $107.28 SLE: 0.19 Item: Celestron - PowerSee...\u001b[0m\n",
            "\u001b[92m247: Guess: $278.51 Truth: $249.99 Error: $28.52 SLE: 0.01 Item: NHOPEEW Android Car ...\u001b[0m\n",
            "\u001b[93m248: Guess: $139.01 Truth: $64.12 Error: $74.89 SLE: 0.59 Item: Other Harmonica A)\n",
            "F...\u001b[0m\n",
            "\u001b[91m249: Guess: $276.92 Truth: $114.99 Error: $161.93 SLE: 0.76 Item: Harley Air Filter Ve...\u001b[0m\n",
            "\u001b[91m250: Guess: $345.09 Truth: $926.00 Error: $580.91 SLE: 0.97 Item: Elite Screens Edge F...\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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        "Tester.test(improved_model_predict, test)\n"
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